The Philosophy of Neuroscience

Over the past three decades, philosophy of science has grown
increasingly “local.” Concerns have switched from general
features of scientific practice to concepts, issues, and puzzles
specific to particular disciplines. Philosophy of neuroscience is a
natural result. This emerging area was also spurred by remarkable
recent growth in the neurosciences. Cognitive and computational
neuroscience continues to encroach upon issues traditionally addressed
within the humanities, including the nature of consciousness, action,
knowledge, and normativity. Empirical discoveries about brain
structure and function suggest ways that “naturalistic”
programs might develop in detail, beyond the abstract philosophical
considerations in their favor.

The literature distinguishes “philosophy of neuroscience”
and “neurophilosophy.” The former concerns foundational
issues within the neurosciences. The latter concerns application of
neuroscientific concepts to traditional philosophical
questions. Exploring various concepts of representation employed in
neuroscientific theories is an example of the former. Examining
implications of neurological syndromes for the concept of a unified
self is an example of the latter. In this entry, we will assume this
distinction and discuss examples of both.

Contrary to some opinion, actual neuroscientific discoveries have
exerted little influence on the details of materialist philosophies of
mind. The “neuroscientific milieu” of the past four
decades has made it harder for philosophers to adopt dualism. But even
the “type-type” or “central state” identity
theories that rose to brief prominence in the late 1950s (Place, 1956;
Smart, 1959) drew upon few actual details of the emerging
neurosciences. Recall the favorite early example of a psychoneural
identity claim: pain is identical to C-fiber firing. The “C
fibers” turned out to be related to only a single aspect of pain
transmission (Hardcastle, 1997). Early identity theorists did not
emphasize psychoneural identity hypotheses, admitting that their
“neuro” terms were placeholders for concepts from future
neuroscience. Their arguments and motivations were philosophical,
even if the ultimate justification of the program was held to be
empirical.

The apology for this lacuna by early identity theorists was that
neuroscience at that time was too nascent to provide any plausible
identities. But potential identities were afoot. David Hubel and
Torsten Wiesel's (1962) electrophysiological demonstrations of the
receptive field properties of visual neurons had been reported with
great fanfare. Using their techniques, neurophysiologists began
discovering neurons throughout visual cortex responsive to
increasingly abstract features of visual stimuli: from edges to motion
direction to colors to properties of faces and hands. More notably,
Donald Hebb had published The Organization of Behavior (1949)
a decade earlier. Therein he offered detailed explanations of
psychological phenomena in terms of known neural mechanisms and
anatomical circuits. His psychological explananda included features
of perception, learning, memory, and even emotional disorders. He
offered these explanations as potential identities. (See the
Introduction to his 1949). One philosopher who did take note of some
available neuroscientific detail was Barbara Von Eckardt-Klein
(1975). She discussed the identity theory with respect to sensations
of touch and pressure, and incorporated then-current hypotheses about
neural coding of sensation modality, intensity, duration, and location
as theorized by Mountcastle, Libet, and Jasper. Yet she was a glaring
exception. By and large, available neuroscience at the time was
ignored by both philosophical friends and foes of early identity
theories.

Philosophical indifference to neuroscientific detail became
“principled” with the rise and prominence of functionalism in the
1970s. The functionalists' favorite argument was based on multiple
realizability: a given mental state or event can be realized in a wide
variety of physical types (Putnam, 1967; Fodor, 1974). So a detailed
understanding of one type of realizing physical system (e.g., brains)
will not shed light on the fundamental nature of mind. A psychological
state-type is autonomous from any single type of its possible
realizing physical mechanisms. (See the entry on “Multiple
Realizability” in this Encyclopedia, linked below.) Instead of
neuroscience, scientifically-minded philosophers influenced by
functionalism sought evidence and inspiration from cognitive
psychology and “program-writing” artificial intelligence. These
disciplines abstract away from underlying physical mechanisms and
emphasize the “information-bearing” properties and capacities of
representations (Haugeland, 1985). At this same time neuroscience was
delving directly into cognition, especially learning and memory. For
example, Eric Kandel (1976) proposed presynaptic mechanisms governing
transmitter release rate as a cell-biological explanation of simple
forms of associative learning. With Robert Hawkins (1984) he
demonstrated how cognitivist aspects of associative learning (e.g.,
blocking, second-order conditioning, overshadowing) could be explained
cell-biologically by sequences and combinations of these basic forms
implemented in higher neural anatomies. Working on the post-synaptic
side, neuroscientists began unraveling the cellular mechanisms of long
term potentiation (LTP) (Bliss and Lomo, 1973). Physiological
psychologists quickly noted its explanatory potential for various
forms of learning and
memory.[1]
Yet few “materialist” philosophers paid
any attention. Why should they? Most were convinced functionalists.
They believed that the “engineering level” details might be important
to the clinician, but were irrelevant to the theorist of mind.

A major turning point in philosophers' interest in neuroscience came
with the publication of Patricia Churchland's Neurophilosophy
(1986). The Churchlands (Patricia and Paul) were already notorious
for advocating eliminative materialism (see the next section). In her
(1986) book, Churchland distilled eliminativist arguments of the past
decade, unified the pieces of the philosophy of science underlying
them, and sandwiched the philosophy between a five-chapter
introduction to neuroscience and a 70-page chapter on three
then-current theories of brain function. She was unapologetic about
her intent. She was introducing philosophy of science to
neuroscientists and neuroscience to philosophers. Nothing could be
more obvious, she insisted, than the relevance of empirical facts
about how the brain works to concerns in the philosophy of mind. Her
term for this interdisciplinary method was “co-evolution” (borrowed
from biology). This method seeks resources and ideas from anywhere on
the theory hierarchy above or below the question at issue. Standing on
the shoulders of philosophers like Quine and Sellars, Churchland
insisted that specifying some point where neuroscience ends and
philosophy of science begins is hopeless because the boundaries are
poorly defined. Neurophilosophers would pick and choose resources from
both disciplines as they saw fit.

Three themes predominate Churchland's philosophical discussion:
developing an alternative to the logical empiricist theory of
intertheoretic reduction; responding to property-dualistic arguments
based on subjectivity and sensory qualia; and responding to
anti-reductionist multiple realizability arguments. These projects
have remained central to neurophilosophy over the past decade. John
Bickle (1998) extends the principal insight of Clifford Hooker's
(1981) post-empiricist theory of intertheoretic reduction. He
quantifies key notions using a model-theoretic account of theory
structure adapted from the structuralist program in philosophy of
science (Balzer, Moulines, and Sneed, 1987). He also makes explicit
the form of argument scientists employ to draw ontological conclusions
(cross-theoretic identities, revisions, or eliminations) based on the
nature of the intertheoretic reduction relations obtaining in specific
cases. For example, physicists concluded that visible light, a
theoretical posit of optics, is electromagnetic radiation within
specified wavelengths, a theoretical posit of electromagnetism: a
cross-theoretic ontological identity. In another case, however,
chemists concluded that phlogiston did not exist: an elimination of a
kind from our scientific ontology. Bickle explicates the nature of
the reduction relation in a specific case using a semi-formal account
of ‘intertheoretic approximation’ inspired by
structuralist results. Paul Churchland (1996) has carried on the
attack on property-dualistic arguments for the irreducibility of
conscious experience and sensory qualia. He argues that acquiring some
knowledge of existing sensory neuroscience increases one's ability to
‘imagine’ or ‘conceive of’ a comprehensive
neurobiological explanation of consciousness. He defends this
conclusion using a thought-experiment based on the history of optics
and electromagnetism. Finally, the literature critical of the multiple
realizability argument has begun to flourish. Although the multiple
realizability argument remains influential among nonreductive
physicalists, it no longer commands the universal acceptance it once
did. Replies to the multiple realizability argument based on
neuroscientific details have appeared. For example, William Bechtel
and Jennifer Mundale (1999) argue that neuroscientists use
psychological criteria in brain mapping studies. This fact undercuts
the likelihood that psychological kinds are multiply realized. (For a
review of recent developments see the final sections of the entry on
‘Multiple Realizability’ in this Encyclopedia, linked
below.)

Eliminative materialism (EM) is the conjunction of two claims. First,
our common sense ‘belief-desire’ conception of mental
events and processes, our ‘folk psychology,’ is a false
and misleading account of the causes of human behavior. Second, like
other false conceptual frameworks from both folk theory and the
history of science, it will be replaced by, rather than smoothly
reduced or incorporated into, a future neuroscience. According to
Churchland, folk psychology is the collection of common homilies about
the causes of human behavior. You ask me why Marica is not
accompanying me this evening. I reply that her grant deadline is
looming. You nod sympathetically. You understand my explanation
because you share with me a generalization that relates beliefs about
looming deadlines, desires about meeting professionally and
financially significant ones, and ensuing free-time behavior. It is
the collection of these kinds of homilies that EM claims to be flawed
beyond significant revision. Although this example involves only
beliefs and desires, folk psychology contains an extensive repertoire
of propositional attitudes in its explanatory nexus: hopes,
intentions, fears, imaginings, and more. To the extent that scientific
psychology (and neuroscience!) retains folk concepts, EM applies to it
as well.

EM is physicalist in the classical sense, postulating some future
brain science as the ultimately correct account of (human)
behavior. It is eliminative in predicting the future removal of folk
psychological kinds from our post-neuroscientific ontology. EM
proponents often employ scientific analogies (Feyerabend 1963;
Churchland, 1981). Oxidative reactions as characterized within
elemental chemistry bear no resemblance to phlogiston release. Even
the “direction” of the two processes differ. Oxygen is
gained when an object burns (or rusts), phlogiston was said to be
lost. The result of this theoretical change was the elimination of
phlogiston from our scientific ontology. There is no such thing. For
the same reasons, according to EM, continuing development in
neuroscience will reveal that there are no such things as beliefs and
desires as characterized by common sense.

Here we focus only on the way that neuroscientific results have
shaped the arguments for EM. Surprisingly, only one argument has been
strongly influenced. (Most arguments for EM stress the failures of folk
psychology as an explanatory theory of behavior.) This argument is
based on a development in cognitive and computational neuroscience that
might provide a genuine alternative to the representations and
computations implicit in folk psychological generalizations. Many
eliminative materialists assume that folk psychology is committed to
propositional representations and computations over their contents that
mimic logical inferences (Paul Churchland, 1981; Stich, 1983; Patricia
Churchland,
1986).[2]
Even though discovering such an
alternative has been an eliminativist goal for some time, neuroscience
only began delivering on this goal over the past fifteen years. Points
in and trajectories through vector spaces, as an interpretation of
synaptic events and neural activity patterns in biological neural
networks are key features of this new development. This argument for EM
hinges on the differences between these notions of cognitive
representation and the propositional attitudes of folk psychology
(Churchland, 1987). However, this argument will be opaque to those with
no background in contemporary cognitive and computational neuroscience,
so we need to present a few scientific details. With these details in
place, we will return to this argument for EM (five paragraphs
below).

At one level of analysis the basic computational element of a neural
network (biological or artificial) is the neuron. This analysis treats
neurons as simple computational devices, transforming inputs into
output. Both neuronal inputs and outputs reflect biological variables.
For the remainder of this discussion, we will assume that neuronal
inputs are frequencies of action potentials (neuronal “spikes”) in the
axons whose terminal branches synapse onto the neuron in question.
Neuronal output is the frequency of action potentials in the axon of
the neuron in question. A neuron computes its total input (usually
treated mathematically as the sum of the products of the signal
strength along each input line times the synaptic weight on that line).
It then computes a new activation state based on its total input and
current activation state, and a new output state based on its new
activation value. The neuron's output state is transmitted as a signal
strength to whatever neurons its axon synapses on. The output state
reflects systematically the neuron's new activation
state.[3]

Analyzed at this level, both biological and artificial neural
networks are interpreted naturally as vector-to-vector
transformers. The input vector consists of values reflecting
activity patterns in axons synapsing on the network's neurons from
outside (e.g., from sensory transducers or other neural networks). The
output vector consists of values reflecting the activity patterns
generated in the network's neurons that project beyond the net (e.g.,
to motor effectors or other neural networks). Given that neurons'
activity depends partly upon their total input, and total input depends
partly on synaptic weights (e.g., presynaptic neurotransmitter release
rate, number and efficacy of postsynaptic receptors, availability of
enzymes in synaptic cleft), the capacity of biological networks to
change their synaptic weights make them plastic
vector-to-vector transformers. In principle, a biological network with
plastic synapses can come to implement any vector-to-vector
transformation that its composition permits (number of input units,
output units, processing layers, recurrency, cross-connections, etc.)
(Churchland, 1987).

The anatomical organization of the cerebellum provides a clear
example of a network amendable to this computational interpretation.
Consider
Figure 1.
The cerebellum is the
bulbous convoluted structure dorsal to the brainstem. A variety of
studies (behavioral, neuropsychological, single-cell
electrophysiological) implicate this structure in motor integration and
fine motor coordination. Mossy fibers (axons) from neurons outside the
cerebellum synapse on cerebellar granule cells, which in turn project
to parallel fibers. Activity patterns across the collection of mossy
fibers (frequency of action potentials per time unit in each fiber
projecting into the cerebellum) provide values for the input vector.
Parallel fibers make multiple synapses on the dendritic trees and cell
bodies of cerebellular Purkinje neurons. Each Purkinje neuron “sums”
its post-synaptic potentials (PSPs) and emits a train of action
potentials down its axon based (partly) on its total input and previous
activation state. Purkinje axons project outside the cerebellum. The
network's output vector is thus the ordered values representing the
pattern of activity generated in each Purkinje axon. Changes to the
efficacy of individual synapses on the parallel fibers and the Purkinje
neurons alter the resulting PSPs in Purkinje axons, generating
different axonal spiking frequencies. Computationally, this amounts to
a different output vector to the same input activity pattern
(plasticity).[4]

This interpretation puts the useful mathematical resources of
dynamical systems into the hands of computational
neuroscientists. Vector spaces are an example. For example,
learning can be characterized fruitfully in terms of changes in
synaptic weights in the network and subsequent reduction of error in
network output. (This approach goes back to Hebb, 1949, although within
the vector-space interpretation that follows.) A useful representation
of this account is on a synaptic weight-error space, where one
dimension represents the global error in the network's output to a
given task, and all other dimensions represent the weight values of
individual synapses in the network. Consider
Figure 2.
Points in this multi-dimensional
state space represent the global performance error correlated with each
possible collection of synaptic weights in the network. As the weights
change with each performance (in accordance with a
biologically-implemented learning algorithm), the global error of
network performance continually decreases. Learning is represented as
synaptic weight changes correlated with a descent along the error
dimension in the space (Churchland and Sejnowski, 1992).
Representations (concepts) can be portrayed as partitions in
multi-dimensional vector spaces. An example is a neuron
activation vector space. See
Figure 3.
A graph of such a space contains one dimension for the activation value
of each neuron in the network (or some subset). A point in this space
represents one possible pattern of activity in all neurons in the
network. Activity patterns generated by input vectors that the network
has learned to group together will cluster around a (hyper-) point or
subvolume in the activity vector space. Any input pattern sufficiently
similar to this group will produce an activity pattern lying in
geometrical proximity to this point or subvolume. Paul Churchland
(1989) has argued that this interpretation of network activity provides
a quantitative, neurally-inspired basis for prototype theories of
concepts developed recently in cognitive psychology.

Using this theoretical development, Paul Churchland (1987, 1989) has
offered a novel argument for EM. According to this approach, activity
vectors are the central kind of representation and vector-to-vector
transformations are the central kind of computation in the brain. This
contrasts sharply with the propositional representations and
logical/semantic computations postulated by folk psychology.
Vectorial content is unfamiliar and alien to common sense. This
cross-theoretic difference is at least as great as that between
oxidative and phlogiston concepts, or kinetic-corpuscular and caloric
fluid heat concepts. Phlogiston and caloric fluid are two “parade”
examples of kinds eliminated from our scientific ontology due to the
nature of the intertheoretic relation obtaining between the theories
with which they are affiliated and the theories that replaced these.
The structural and dynamic differences between the folk psychological
and emerging cognitive neuroscientific kinds suggest that the theories
affiliated with the latter will also correct significantly the theory
affiliated with the former. This is the key premise of an eliminativist
argument based on predicted intertheoretic relations. And these
intertheoretic contrasts are no longer just an eliminativist's goal.
Computational and cognitive neuroscience has begun to deliver an
alternative kinematics for cognition, one that provides no structural
analogue for the propositional attitudes.

Certainly the replacement of propositional contents by vectorial
alternatives implies significant correction to folk psychology. But
does it justify EM? Even though this central feature of
folk-psychological posits finds no analogues in one hot theoretical
development in recent cognitive and computational neuroscience, there
might be other aspects of cognition that folk psychology gets right.
Within neurophilosophy, concluding that a cross-theoretic identity
claim is true (e.g., folk psychological state F is identical to neural
state N) or that an eliminativist claim is true (there is no such thing
as folk psychological state F) depends on the nature of the
intertheoretic reduction obtaining between the theories affiliated with
the posits in question (Hooker, 1981; Churchland, 1986; Bickle, 1998).
But the underlying account of intertheoretic reduction recognizes a
spectrum of possible reductions, ranging from relatively “smooth”
through “significantly revisionary” to “extremely
bumpy”.[5]
Might
the reduction of folk psychology and a “vectorial”
neurobiology occupy the middle ground between “smooth” and
“bumpy” intertheoretic reductions, and hence suggest a
“revisionary” conclusion? The reduction of classical
equilibrium thermodynamics to statistical mechanics to microphysics
provides a potential analogy. John Bickle (1992, 1998, chapter 6)
argues on empirical grounds that such a outcome is likely. He
specifies conditions on “revisionary” reductions from
historical examples and suggests that these conditions are obtaining
between folk psychology and cognitive neuroscience as the latter
develops. In particular, folk psychology appears to have gotten right
the grossly-specified functional profile of many cognitive states,
especially those closely related to sensory input and behavioral
output. It also appears to get right the “intentionality”
of many cognitive states—the object that the state is of or
about—even though cognitive neuroscience eschews its implicit
linguistic explanation of this feature. Revisionary physicalism
predicts significant
conceptual change to folk psychological concepts, but denies
total elimination of the caloric fluid-phlogiston variety.

The philosophy of science is another area where vector space
interpretations of neural network activity patterns has impacted
philosophy. In the Introduction to his (1989) book, A
Neurocomputational Perspective, Paul Churchland asserts that it
will soon be impossible to do serious work in the philosophy of
science without drawing on empirical work in the brain and behavioral
sciences. To justify this claim, in Part II of the book he suggests
neurocomputational reformulations of key concepts from this area. At
the heart is a neurocomputational account of the structure of
scientific theories (1989, chapter 9). Problems with the orthodox
“sets-of-sentences” view have been well-known for over
three decades. Churchland advocates replacing the orthodox view with
one inspired by the “vectorial” interpretation of neural
network activity. Representations implemented in neural networks (as
discussed above) compose a system that corresponds to important
distinctions in the external environment, are not explicitly
represented as such within the input corpus, and allow the trained
network to respond to inputs in a fashion that continually reduces
error. These are exactly the functions of theories. Churchland is bold
in his assertion: an individual's theory-of-the-world is a specific
point in that individual's error-synaptic weight vector space. It is a
configuration of synaptic weights that partitions the individual's
activation vector space into subdivisions that reduce future error
messages to both familiar and novel inputs. (Consider again
Figure 2
and
Figure 3.)
This reformulation invites an objection, however. Churchland boasts
that his theory of theories is preferable to existing alternatives to
the orthodox “sets-of-sentences” account—for
example, the semantic view (Suppe, 1974; van Fraassen,
1980)—because his is closer to the “buzzing brains”
that use theories. But as Bickle (1993) notes, neurocomputational
models based on the mathematical resources described above are a long
way into the realm of abstractia. Even now, they remain little more
than novel (and suggestive) applications of the mathematics of
quasi-linear dynamical systems to simplified schemata of brain
circuitries. Neurophilosophers owe some account of identifications
across ontological categories before the philosophy of science
community will accept the claim that theories are points in
high-dimensional state spaces implemented in biological neural
networks. (There is an important methodological assumption lurking in
this objection, however, which we will discuss toward the end of the
next paragraph.)

Churchland's neurocomputational reformulations of scientific and
epistemological concepts build on this account of theories. He
sketches “neuralized” accounts of the theory-ladenness of
perception, the nature of concept unification, the virtues of
theoretical simplicity, the nature of Kuhnian paradigms, the
kinematics of conceptual change, the character of abduction, the
nature of explanation, and even moral knowledge and epistemological
normativity. Conceptual redeployment, for example, is the activation
of an already-existing prototype representation—the centerpoint
or region of a partition of a high-dimensional vector space in a
trained neural network—to a novel type of input
pattern. Obviously, we can't here do justice to Churchland's many and
varied attempts at reformulation. We urge the intrigued reader to
examine his suggestions in their original form. But a word about
philosophical methodology is in order. Churchland is
not attempting “conceptual analysis” in anything
resembling its traditional philosophical sense and neither, typically,
are neurophilosophers.(This is why a discussion of neurophilosophical
reformulations fits with a discussion of EM.) There are philosophers
who take the discipline's ideal to be a relatively simple set of
necessary and sufficient conditions, expressed in non-technical
natural language, governing the application of important concepts
(like justice, knowledge, theory, or explanation). These analyses
should square, to the extent possible, with pretheoretical
usage. Ideally, they should preserve synonymy. Other philosophers view
this ideal as sterile, misguided, and perhaps deeply mistaken about
the underlying structure of human knowledge (Ramsey,
1992). Neurophilosophers tend to reside in the latter camp. Those who
dislike philosophical speculation about the promise and potential of
nascent science in an effort to reformulate
(“reform-ulate”) traditional philosophical
concepts have probably already discovered that neurophilosophy is not
for them. But the charge that neurocomputational reformulations of the
sort Churchland attempts are “philosophically
uninteresting” or “irrelevant” because they fail to
provide “adequate analyses” of theory, explanation, and
the like will fall on deaf ears among many contemporary philosophers,
as well as their cognitive-scientific and neuroscientific friends.

Before we leave the neurophilosophical applications of this
theoretical development from recent cognitive/computational
neuroscience, one more point of scientific detail is in
order. Many neural modelers no longer treat the neuron as the
basic computational unit in the brain. Compartmental modeling
enables computational neuroscientists to mimic activity in and
interactions between patches of neuronal membrane (Bower and Beeman,
1995). This permits modelers to control and manipulate a variety of
subcellular factors that determine action potentials per time unit
(including the topology of membrane structure in individual neurons,
variations in ion channels across membrane patches, field properties
of post-synaptic potentials depending on the location of the synapse
on the dendrite or soma). Modelers can “custom build” the
neurons in their target circuitry without sacrificing the ability to
study circuit properties of networks. For these reasons, many serious
computational neuroscientists now work at a level that treats
neurons as structured computational devices. With compartmental
modeling, not only are simulated neural networks interpretable as
vector-to-vector transformers. The neurons composing them are,
too.

Philosophy of science and scientific epistemology are not the only
areas where philosophers have lately urged the relevance of
neuroscientific discoveries. Kathleen Akins (1996) argues that a
“traditional” view of the senses underlies the variety of
sophisticated “naturalistic” programs about
intentionality. (She cites the Churchlands, Daniel Dennett, Fred
Dretske, Jerry Fodor, David Papineau, Dennis Stampe, and Kim Sterelny
as examples, with extensive references.) Current neuroscientific
understanding of the mechanisms and coding strategies implemented by
sensory receptors shows that this traditional view is mistaken. The
traditional view holds that sensory systems are
“veridical” in at least three ways. (1) Each signal in the
system correlates with a small range of properties in the external (to
the body) environment. (2) The structure in the relevant relations
between the external properties the receptors are sensitive to is
preserved in the structure of the relations between the resulting
sensory states. And (3) the sensory system reconstructs faithfully,
without fictive additions or embellishments, the external
events. Using recent neurobiological discoveries about response
properties of thermal receptors in the skin as an illustration, Akins
shows that sensory systems are “narcissistic” rather than
“veridical.” All three traditional assumptions are
violated. These neurobiological details and their philosophical
implications open novel questions for the philosophy of perception and
for the appropriate foundations for naturalistic projects about
intentionality. Armed with the known neurophysiology of sensory
receptors, for example, our “philosophy of perception” or
of “perceptual intentionality” will no longer focus on the
search for correlations between states of sensory systems and
“veridically detected” external properties. This
traditional philosophical (and scientific!) project rests upon a
mistaken “veridical” view of the senses. Neuroscientific
knowledge of sensory receptor activity also shows that sensory
experience does not serve the naturalist well as a “simple
paradigm case” of an intentional relation between representation
and world. Once again, available scientific detail shows the naivity
of some traditional philosophical projects.

Focusing on the anatomy and physiology of the pain transmission
system, Valerie Hardcastle (1997) urges a similar negative implication
for a popular methodological assumption. Pain experiences have long
been philosophers' favorite cases for analysis and theorizing about
conscious experience generally. Nevertheless, every position about pain
experiences has been defended recently: eliminativism, a variety of
objectivist views, relational views, and subjectivist views. Why so
little agreement, despite agreement that pain experiences are the place
to start an analysis or theory of consciousness? Hardcastle urges two
answers. First, philosophers tend to be uninformed about the neuronal
complexity of our pain transmission systems, and build their analyses
or theories on the outcome of a single component of a multi-component
system. Second, even those who understand some of the underlying
neurobiology of pain tend to advocate gate-control
theories.[6]
But the
best existing gate-control theories are vague about the neural
mechanisms of the gates. Hardcastle instead proposes a dissociable dual
system of pain transmission, consisting of a pain sensory system
closely analogous in its neurobiological implementation to other
sensory systems, and a descending pain inhibitory system. She argues
that this dual system is consistent with recent neuroscientific
discoveries and accounts for all the pain phenomena that have tempted
philosophers toward particular (but limited) theories of pain
experience. The neurobiological uniqueness of the pain inhibitory
system, contrasted with the mechanisms of other sensory modalities,
renders pain processing atypical. In particular, the pain inhibitory
system dissociates pain sensation from stimulation of nociceptors (pain
receptors). Hardcastle concludes from the neurobiological uniqueness of
pain transmission that pain experiences are atypical conscious events,
and hence not a good place to start theorizing about or analyzing the
general type.

Developing and defending theories of content is a central topic in
current philosophy of mind. A common desideratum in this debate is a
theory of cognitive representation consistent with a physical or
naturalistic ontology. We'll here describe a few contributions
neurophilosophers have made to this literature.

When one perceives or remembers that he is out of coffee, his brain
state possesses intentionality or “aboutness.” The percept or memory
is about one's being out of coffee; it represents one as being out of
coffee. The representational state has content. A psychosemantics
seeks to explain what it is for a representational state to be about
something: to provide an account of how states and events can have
specific representational content. A physicalist psychosemantics seeks
to do this using resources of the physical sciences exclusively.
Neurophilosophers have contributed to two types of physicalist
psychosemantics: the Functional Role approach and the Informational
approach. For a description of these and other theories of mental
content, see the entries on
causal theories of mental content,
mental representation, and
teleological theories of mental content.

The core claim of a functional role semantics holds that a
representation has its content in virtue of relations it bears to
other representations. Its paradigm application is to concepts of
truth-functional logic, like the conjunctive ‘and’ or
disjunctive ‘or.’ A physical event instantiates the
‘and’ function just in case it maps two true inputs onto a
single true output. Thus it is the relations an expression bears to
others that give it the semantic content of ‘and.’
Proponents of functional role semantics propose similar analyses for
the content of all representations (Block 1986). A physical event
represents birds, for example, if it bears the right relations to
events representing feathers and others representing beaks. By
contrast, informational semantics ascribe content to a state depending
upon the causal relations obtaining between the state and the object
it represents. A physical state represents birds, for example, just in
case an appropriate causal relation obtains between it and birds. At
the heart of informational semantics is a causal account of
information (Dretske, 1981, 1988). Red spots on a face carry the
information that one has measles because the red spots are caused by
the measles virus. A common criticism of informational semantics
holds that mere causal covariation is insufficient for representation,
since information (in the causal sense) is by definition always
veridical while representations can misrepresent. A popular solution
to this challenge invokes a teleological analysis of
‘function.’ A brain state represents X by virtue
of having the function of carrying information about being caused
by X (Dretske 1988). These two approaches do not exhaust the
popular options for a psychosemantics, but are the ones to which
neurophilosophers have contributed.

Paul Churchland's allegiance to functional role semantics goes back to
his earliest views about the semantics of terms in a language. In his
(1979) book, he insists that the semantic identity (content) of a term
derives from its place in the network of sentences of the entire
language. The functional economies envisioned by early functional role
semanticists were networks with nodes corresponding to the objects and
properties denoted by expressions in a language. Thus one node,
appropriately connected, might represent birds, another feathers, and
another beaks. Activation of one of these would tend to spread to the
others. As ‘connectionist’ network modeling developed,
alternatives arose to this one-representation-per-node
‘localist’ approach. By the time Churchland (1989)
provided a neuroscientific elaboration of functional role semantics
for cognitive representations generally, he too had abandoned the
‘localist’ interpretation. Instead, he offered a
‘state-space semantics’.

We saw in the section just above how (vector) state spaces provide a
natural interpretation for activity patterns in neural networks
(biological and artificial). A state-space semantics for cognitive
representations is a species of a functional role semantics because the
individuation of a particular state depends upon the relations
obtaining between it and other states. A representation is a point in
an appropriate state space, and points (or subvolumes) in a space are
individuated by their relations to other points (locations, geometrical
proximity). Churchland (1989, 1995) illustrates a state-space semantics
for neural states by appealing to sensory systems. One popular theory
in sensory neuroscience of how the brain codes for sensory qualities
(like color) is the opponent process account (Hardin 1988).
Churchland (1995) describes a three-dimensional activation vector
state-space in which every color perceivable by humans is represented
as a point (or subvolume). Each dimension corresponds to activity rates
in one of three classes of photoreceptors present in the human retina
and their efferent paths: the red-green opponent pathway, yellow-blue
opponent pathway, and black-white (contrast) opponent pathway. Photons
striking the retina are transduced by the receptors, producing an
activity rate in each of the segregated pathways. A represented color
is hence a triplet of activation frequency rates. As an illustration,
consider again
Figure 3.
Each dimension in
that three-dimensional space will represent average frequency of action
potentials in the axons of one class of ganglion cells projecting out
of the retina. Each color perceivable by humans will be a region of
that space. For example, an orange stimulus produces a relatively low
level of activity in both the red-green and yellow-blue opponent
pathways (x-axis and y-axis, respectively),
and middle-range activity
in the black-white (contrast) opponent pathway (z-axis). Pink stimuli,
on the other hand, produce low activity in the red-green opponent
pathway, middle-range activity in the yellow-blue opponent pathway, and
high activity in the black-white (contrast) opponent
pathway.[7]
The
location of each color in the space generates a ‘color
solid.’ Location on the solid and geometrical proximity between
regions reflect structural similarities between the perceived colors.
Human gustatory representations are points in a four-dimensional state
space, with each dimension coding for activity rates generated by
gustatory stimuli in each type of taste receptor (sweet, salty, sour,
bitter) and their segregated efferent pathways. When implemented in a
neural network with structural and hence computational resources as
vast as the human brain, the state space approach to psychosemantics
generates a theory of content for a huge number of cognitive
states.[8]

Jerry Fodor and Ernest LePore (1992) raise an important challenge to
Churchland's psychosemantics. Location in a state space alone seems
insufficient to fix a state's representational content. Churchland
never explains why a point in a three-dimensional state space
represents a color, as opposed to any other quality, object,
or event that varies along three
dimensions.[9].
Churchland's account
achieves its explanatory power by the interpretation imposed on the
dimensions. Fodor and LePore allege that Churchland never specifies how
a dimension comes to represent, e.g., degree of saltiness, as opposed
to yellow-blue wavelength opposition. One obvious answer appeals to the
stimuli that form the ‘external’ inputs to the neural
network in question. Then, for example, the individuating conditions on
neural representations of colors are that opponent processing neurons
receive input from a specific class of photoreceptors. The latter in
turn have electromagnetic radiation (of a specific portion of the
visible spectrum) as their activating stimuli. However, this appeal to
‘external’ stimuli as the ultimate individuating conditions
for representational content makes the resulting approach a version of
informational semantics. Is this approach consonant with other
neurobiological details?

The neurobiological paradigm for informational semantics is the
feature detector: one or more neurons that are (i) maximally
responsive to a particular type of stimulus, and (ii) have the function
of indicating the presence of that stimulus type. Examples of such
stimulus-types for visual feature detectors include high-contrast
edges, motion direction, and colors. A favorite feature detector among
philosophers is the alleged fly detector in the frog. Lettvin et
al. (1959) identified cells in the frog retina that responded
maximally to small shapes moving across the visual field. The idea that
these cells' activity functioned to detect flies rested upon knowledge
of the frogs' diet. (Bechtel 1998 provides a useful discussion.) Using
experimental techniques ranging from single-cell recording to
sophisticated functional imaging, neuroscientists have recently
discovered a host of neurons that are maximally responsive to a variety
of stimuli. However, establishing condition (ii) on a feature detector
is much more difficult. Even some paradigm examples have been called
into question. David Hubel and Torsten Wiesel's (1962) Nobel
Prize-winning work establishing the receptive fields of neurons in
striate cortex is often interpreted as revealing cells whose function
is edge detection. However, Lehky and Sejnowski (1988) have challenged
this interpretation. They trained an artificial neural network to
distinguish the three-dimensional shape and orientation of an object
from its two-dimensional shading pattern. Their network incorporates
many features of visual neurophysiology. Nodes in the trained network
turned out to be maximally responsive to edge contrasts, but did not
appear to have the function of edge detection. (See Churchland and
Sejnowski 1992 for a review.)

Kathleen Akins (1996) offers a different neurophilosophical challenge
to informational semantics and its affiliated feature-detection view
of sensory representation. We saw in the previous section how Akins
argues that the physiology of thermoreception violates three necessary
conditions on ‘veridical’ representation. From this fact
she draws doubts about looking for feature detecting neurons to ground
a psychosemantics generally, including thought contents. Human
thoughts about flies, for example, are sensitive to numerical
distinctions between particular flies and the particular locations
they can occupy. But the ends of frog nutrition are well served
without a representational system sensitive to such ontological
refinements. Whether a fly seen now is numerically identical to one
seen a moment ago need not, and perhaps cannot, figure into the frog's
feature detection repertoire. Akins' critique casts doubt on whether
details of sensory transduction will scale up to provide an adequate
unified psychosemantics. It also raises new questions for human
intentionality. How do we get from activity patterns in
“narcissistic” sensory receptors, keyed not to
“objective” environmental features but rather only to
effects of the stimuli on the patch of tissue innervated, to the human
ontology replete with enduring objects with stable configurations of
properties and relations, types and their tokens (as the
“fly-thought” example presented above reveals), and the
rest? And how did the development of a stable, rich ontology confer
survival advantages to human ancestors?

Consciousness has re-emerged as a topic in philosophy of mind and the
cognitive and brain sciences over the past three decades. Instead of
ignoring it, many physicalists now seek to explain it (Dennett, 1991).
Here we focus exclusively on ways that neuroscientific discoveries have
impacted philosophical debates about the nature of consciousness and
its relation to physical mechanisms. (See links to other entries in
this encyclopedia below for broader discussions about consciousness and
physicalism.)

Thomas Nagel (1974) argues that conscious experience is subjective,
and thus permanently recalcitrant to objective scientific
understanding. He invites us to ponder ‘what it is like to be a
bat’ and urges the intuition that no amount of
physical-scientific knowledge (including neuroscientific) supplies a
complete answer. Nagel's intuition pump has generated extensive
philosophical discussion. At least two well-known replies make direct
appeal to neurophysiology. John Biro (1991) suggests that part of the
intuition pumped by Nagel, that bat experience is substantially
different from human experience, presupposes systematic relations
between physiology and phenomenology. Kathleen Akins (1993a) delves
deeper into existing knowledge of bat physiology and reports much that
is pertinent to Nagel's question. She argues that many of the
questions about bat subjectivity that we still consider open hinge on
questions that remain unanswered about neuroscientific details. One
example of the latter is the function of various cortical activity
profiles in the active bat.

More recently philosopher David Chalmers (1996) has argued that any
possible brain-process account of consciousness will leave open an
‘explanatory gap’ between the brain process and properties
of the conscious
experience.[10]
This is because no
brain-process theory can answer the “hard” question: Why
should that particular brain process give rise to conscious
experience? We can always imagine (“conceive of”) a
universe populated by creatures having those brain processes but
completely lacking conscious experience. A theory of consciousness
requires an explanation of how and why some brain process causes
consciousness replete with all the features we commonly
experience. The fact that the hard question remains unanswered shows
that we will probably never get a complete explanation of
consciousness at the level of neural mechanism. Paul and Patricia
Churchland (1997) have recently offered the following diagnosis and
reply. Chalmers offers a conceptual argument, based on our
ability to imagine creatures possessing brains like ours but wholly
lacking in conscious experience. But the more one learns about how the
brain produces conscious experience—and a literature is
beginning to emerge (e.g., Gazzaniga, 1995)—the harder it
becomes to imagine a universe consisting of creatures with brain
processes like ours but lacking consciousness. This is not just bare
assertion. The Churchlands appeal to some neurobiological detail. For
example, Paul Churchland (1995) develops a neuroscientific account of
consciousness based on recurrent connections between thalamic nuclei
(particularly “diffusely projecting” nuclei like the
intralaminar nuclei) and
cortex.[11]
Churchland argues that the thalamocortical recurrency accounts for the
selective features of consciousness, for the effects of short-term
memory on conscious experience, for vivid dreaming during REM
(rapid-eye movement) sleep, and other “core” features of
conscious experience. In other words, the Churchlands are claiming
that when one learns about activity patterns in these recurrent
circuits, one can't “imagine” or “conceive of”
this activity occurring without these core features of conscious
experience. (Other than just mouthing the words, “I am now
imagining activity in these circuits without selective attention/the
effects of short-term memory/vivid dreaming/...”).

A second focus of skeptical arguments about a complete
neuroscientific explanation of consciousness is sensory
qualia: the introspectable qualitative aspects of sensory
experience, the features by which subjects discern similarities and
differences among their experiences. The colors of visual sensations
are a philosopher's favorite example. One famous puzzle about color
qualia is the alleged conceivability of spectral inversions. Many
philosophers claim that it is conceptually possible (if perhaps
physically impossible) for two humans not to differ
neurophysiologically, while the color that fire engines and tomatoes
appear to have to one subject is the color that grass and frogs appear
to have to the other (and vice versa). A large amount of
neuroscientifically-informed philosophy has addressed this question.
(C.L. Hardin 1988 and Austen Clark 1993 are noteworthy examples.) A
related area where neurophilosophical considerations have emerged
concerns the metaphysics of colors themselves (rather than color
experiences). A longstanding philosophical dispute is whether colors
are objective properties existing external to perceivers or rather
identifiable as or dependent upon minds or nervous systems. Some recent
work on this problem begins with characteristics of color experiences:
for example, that color similarity judgments produce color orderings
that align on a circle (Clark 1993). With this resource, one can seek
mappings of phenomenology onto environmental or physiological
regularities. Identifying colors with particular frequencies of
electromagnetic radiation does not preserve the structure of the hue
circle, whereas identifying colors with activity in opponent processing
neurons does. Such a tidbit is not decisive for the color
objectivist-subjectivist debate, but it does convey the type of
neurophilosophical work being done on traditional metaphysical issues
beyond the philosophy of mind. (For more details on these issues, see
the entry on Color in this Encyclopedia, linked below.)

We saw in the discussion of Hardcastle (1997) two sections above
that neurophilosophers have entered disputes about the nature and
methodological import of pain experiences. Two decades earlier, Dan
Dennett (1978) took up the question of whether it is possible to build
a computer that feels pain. He compares and notes tension between
neurophysiological discoveries and common sense intuitions about pain
experience. He suspects that the incommensurability between scientific
and common sense views is due to incoherence in the latter. His
attitude is wait-and-see. But foreshadowing Churchland's reply to
Chalmers, Dennett favors scientific investigations over
conceivability-based philosophical arguments.

Neurological deficits have attracted philosophical interest. For
thirty years philosophers have found implications for the unity of the
self in experiments with commissurotomy patients (Nagel
1971).[12]
In carefully controlled experiments, commissurotomy patients display
two dissociable seats of consciousness. In chapter 5 of her (1986)
book, Patricia Churchland scouts philosophical implications of a
variety of neurological deficits. One deficit is blindsight. Some
patients with lesions to primary visual cortex report being unable to
see items in regions of their visual fields, yet perform far better
than chance in forced guess trials about stimuli in those regions. A
variety of scientific and philosophical interpretations have been
offered. Ned Block (1988) worries that many of these conflate distinct
notions of consciousness. He labels these notions ‘phenomenal
consciousness’ (‘P-consciousness’) and ‘access
consciousness’ (‘A-consciousness’). The former is
the ‘what it is like’-ness of experience. The latter is
the availability of representational content to self-initiated action
and speech. Block argues that P-consciousness is not always
representational whereas A-consciousness is. Dennett (1991, 1995) and
Michael Tye (1993) are skeptical of non-representational analyses of
consciousness in general. They provide accounts of blindsight that do
not depend on Block's distinction.

We break off our brief overview of neurophilosophical work on
consciousness here. Many other topics are worth neurophilosophical
pursuit. We mentioned commissurotomy and the unity of consciousness and
the self, which continues to generate discussion. Qualia beyond those
of color and pain have begun to attract neurophilosophical attention
(Akins 1993a, 1993b, 1996; Clark 1993), as has self-consciousness
(Bermudez 1998).

One of the first issues to arise in the ‘philosophy of
neuroscience’ (before there was a recognized area) was the
localization of cognitive functions to specific neural regions.
Although the ‘localization’ approach had dubious origins in
the phrenology of Gall and Spurzheim, and was challenged severely by
Flourens throughout the early nineteenth century, it re-emerged in the
study of aphasia by Bouillaud, Auburtin, Broca, and Wernicke. These
neurologists made careful studies (where possible) of linguistic
deficits in their aphasic patients followed by brain autopsies post
mortem.[13]
Broca's initial study of twenty-two
patients in the mid-nineteenth century confirmed that damage to the
left cortical hemisphere was predominant, and that damage to
the second and third frontal convolutions was necessary to produce
speech production deficits. Although the anatomical coordinates Broca
postulated for the ‘speech production center’ do not correlate
exactly with damage producing production deficits, both this area of
frontal cortex and speech production deficits still bear his name
(‘Broca's area’ and ‘Broca's aphasia’). Less
than two decades later Carl Wernicke published evidence for a second
language center. This area is anatomically distinct from Broca's area,
and damage to it produced a very different set of aphasic symptoms. The
cortical area that still bears his name (‘Wernicke's area’)
is located around the first and second convolutions in temporal cortex,
and the aphasia that bears his name (‘Wernicke's aphasia’)
involves deficits in language comprehension. Wernicke's method, like
Broca's, was based on lesion studies: a careful evaluation of the
behavioral deficits followed by post mortem examination to find the
sites of tissue damage and atrophy. Lesion studies suggesting more
precise localization of specific linguistic functions remain a
cornerstone to this day in aphasic research.

Lesion studies have also produced evidence for the localization of
other cognitive functions: for example, sensory processing and certain
types of learning and memory. However, localization arguments for
these other functions invariably include studies using animal
models. With an animal model, one can perform careful behavioral
measures in highly controlled settings, then ablate specific areas of
neural tissue (or use a variety of other techniques to block or
enhance activity in these areas) and remeasure performance on the same
behavioral tests. But since we lack an animal model for (human)
language production and comprehension, this additional evidence isn't
available to the neurologist or neurolinguist. This fact makes the
study of language a paradigm case for evaluating the logic of the
lesion/deficit method of inferring functional
localization. Philosopher Barbara Von Eckardt (1978) attempts to make
explicit the steps of reasoning involved in this common and
historically important method. Her analysis begins with Robert
Cummins' early analysis of functional explanation, but she extends it
into a notion of structurally adequate functional
analysis. These analyses break down a complex capacity C into its
constituent capacities c1,
c2,…, cn,
where the constituent capacities are consistent with the underlying
structural details of the system. For example, human speech production
(complex capacity C) results from formulating a speech intention, then
selecting appropriate linguistic representations to capture the
content of the speech intention, then formulating the motor commands
to produce the appropriate sounds, then communicating these motor
commands to the appropriate motor pathways (constituent capacities
c1, c2,…,
cn). A functional-localization hypothesis
has the form: brain structure S in organism (type) O has constituent
capacity ci, where
ci is a function of some part of O. An
example might be: Broca's area (S) in humans (O) formulates motor
commands to produce the appropriate sounds (one of the constituent
capacities ci). Such hypotheses specify
aspects of the structural realization of a functional-component
model. They are part of the theory of the neural realization of the
functional model.

Armed with these characterizations, Von Eckardt argues that inference
to a functional-localization hypothesis proceeds in two steps. First,
a functional deficit in a patient is hypothesized based on the
abnormal behavior the patient exhibits. Second, localization of
function in normal brains is inferred on the basis of the functional
deficit hypothesis plus the evidence about the site of brain damage.
The structurally-adequate functional analysis of the capacity connects
the pathological behavior to the hypothesized functional deficit. This
connection suggests four adequacy conditions on a functional deficit
hypothesis. First, the pathological behavior P (e.g., the speech
deficits characteristic of Broca's aphasia) must result from failing
to exercise some complex capacity C (human speech production). Second,
there must be a structurally-adequate functional analysis of how
people exercise capacity C that involves some constituent capacity
ci (formulating motor commands to produce
the appropriate sounds). Third, the operation of the steps described
by the structurally-adequate functional analysis minus the operation
of the component performing ci (Broca's
area) must result in pathological behavior P. Fourth, there must not
be a better available explanation for why the patient does
P. Arguments to a functional deficit hypothesis on the basis of
pathological behavior is thus an instance of argument to the best
available explanation. When postulating a deficit in a normal
functional component provides the best available explanation of the
pathological data, we are justified in drawing the inference.

Von Eckardt applies this analysis to a neurological case study
involving a controversial reinterpretation of
agnosia.[14]
Her
philosophical explication of this important neurological method reveals
that most challenges to localization arguments either argue only
against the localization of a particular type of functional capacity or
against generalizing from localization of function in one individual to
all normal individuals. (She presents examples of each from the
neurological literature.) Such challenges do not impugn the validity of
standard arguments for functional localization from deficits. It does
not follow that such arguments are unproblematic. But they face
difficult factual and methodological problems, not logical ones.
Furthermore, the analysis of these arguments as involving a type of
functional analysis and inference to the best available explanation
carries an important implication for the biological study of cognitive
function. Functional analyses require functional theories, and
structurally adequate functional analyses require checks imposed by the
lower level sciences investigating the underlying physical mechanisms.
Arguments to best available explanation are often hampered by a lack of
theoretical imagination: the available explanations are often severely
limited. We must seek theoretical inspiration from any level of theory
and explanation. Hence making explicit the ‘logic’ of this
common and historically important form of neurological explanation
reveals the necessity of joint participation from all scientific
levels, from cognitive psychology down to molecular neuroscience. Von
Eckardt (1978) anticipated what came to be heralded as the
‘co-evolutionary research methodology,’ which remains a
centerpiece of neurophilosophy to the present day.

Over the last two decades, evidence for localization of cognitive
function has come increasingly from a new source: the development and
refinement of neuroimaging techniques. The form of
localization-of-function argument appears not to have changed from that
employing lesion studies (as analyzed by Von Eckardt). Instead, these
imaging technologies resolve some of the methodological problems that
plague lesion studies. For example, researchers do not need to wait
until the patient dies, and in the meantime probably acquires
additional brain damage, to find the lesion sites. Two functional
imaging techniques are prominent: positron emission tomography, or PET,
and functional magnetic resonance imaging, or fMRI. Although these
measure different biological markers of functional activity, both now
have a resolution down to around
1mm.[15]
As these techniques
increase spatial and temporal resolution of functional markers and
continue to be used with sophisticated behavioral methodologies, the
possibility of localizing specific psychological functions to
increasingly specific neural regions continues to
grow.[16]

What we now know about the cellular and molecular mechanisms of neural
conductance and transmission is spectacular. (For those in doubt,
simply peruse for five minutes a recent volume of Society for
Neuroscience Abstracts.) The same evaluation holds for all levels
of explanation and theory about the mind/brain: maps, networks,
systems, and behavior. This is a natural outcome of increasing
scientific specialization. We develop the technology, the experimental
techniques, and the theoretical frameworks within specific disciplines
to push forward our understanding. Still, a crucial aspect of the total
picture gets neglected: the relationship between the levels, the
‘glue’ that binds knowledge of neuron activity to
subcellular and molecular mechanisms, network activity patterns to the
activity of and connectivity between single neurons, and behavior to
network activity. This problem is especially glaring when we focus on
the relationship between ‘cognitivist’ psychological
theories, postulating information-bearing representations and processes
operating over their contents, and the activity patterns in networks of
neurons. Co-evolution between explanatory levels still seems more like
a distant dream rather than an operative methodology.

It is here that some neuroscientists appeal to
‘computational’ methods (Churchland and Sejnowski 1992). If
we examine the way that computational models function in more developed
sciences (like physics), we find the resources of dynamical
systems constantly employed. Global effects (such as large-scale
meteorological patterns) are explained in terms of the interaction of
‘local’ lower-level physical phenomena, but only by
dynamical, nonlinear, and often chaotic sequences and combinations.
Addressing the interlocking levels of theory and explanation in the
mind/brain using computational resources that have worked to bridge
levels in more mature sciences might yield comparable results. This
methodology is necessarily interdisciplinary, drawing on resources and
researchers from a variety of levels, including higher levels like
experimental psychology, ‘program-writing’ and
‘connectionist’ artificial intelligence, and philosophy of
science.

However, the use of computational methods in neuroscience is not
new. Hodgkin, Huxley, and Katz (1952) incorporated values of
voltage-dependent potassium conductance they had measured
experimentally in the squid giant axon into an equation from physics
describing the time evolution of a first-order kinetic process. This
equation enabled them to calculate best-fit curves for modeled
conductance versus time data that reproduced the S-shaped (sigmoidal)
function suggested by their experimental data. Using equations borrowed
from physics, Rall (1959) developed the cable model of dendrites. This
theory provided an account of how the various inputs from across the
dendritic tree interact temporally and spatially to determine the
input-output properties of single neurons. It remains influential
today, and has been incorporated into the GENESIS software for
programming neurally realistic networks (Bower and Beeman 1995). More
recently, David Sparks and his colleagues have shown that a
vector-averaging model of activity in neurons of superior colliculi
correctly predicts experimental results about the amplitude and
direction of saccadic eye movements (Lee, Rohrer, and Sparks 1988).
Working with a more sophisticated mathematical model, Apostolos
Georgopoulos and his colleagues have predicted direction and amplitude
of hand and arm movements based on averaged activity of 224 cells in
motor cortex. Their predictions have borne out under a variety of
experimental tests (Georgopoulos et al. 1986). We mention these
particular studies only because we are familiar with them. We could
multiply examples of the fruitful interaction of computational and
experimental methods in neuroscience easily by one-hundred-fold. Many
of these extend back before ‘computational neuroscience’
was a recognized research endeavor.

We've already seen one example, the vector transformation account,
of neural representation and computation, under active development in
cognitive neuroscience. Other approaches using
‘cognitivist’ resources are also being
pursued.[17]
Many
of these projects draw upon ‘cognitivist’
characterizations of the phenomena to be explained. Many exploit
‘cognitivist’ experimental techniques and methodologies.
Some even attempt to derive ‘cognitivist’ explanations
from cell-biological processes (e.g., Hawkins and Kandel 1984). As
Stephen Kosslyn (1997) puts it, cognitive neuroscientists employ the
‘information processing’ view of the mind characteristic
of cognitivism without trying to separate it from theories of brain
mechanisms. Such an endeavor calls for an interdisciplinary community
willing to communicate the relevant portions of the mountain of detail
gathered in individual disciplines with interested nonspecialists: not
just people willing to confer with those working at related levels,
but researchers trained in the methods and factual details of a
variety of levels. This is a daunting requirement, but it does offer
some hope for philosophers wishing to contribute to future
neuroscience. Thinkers trained in both the ‘synoptic
vision’ afforded by philosophy and the factual and experimental
basis of genuine graduate-level science would be ideally equipped for
this task. Recognition of this potential niche has been slow among
graduate programs in philosophy, but there is some hope that a few
programs are taking steps to fill it. (See, e.g., “Other
Internet Resources,” linked below.)

The distinction between “philosophy of neuroscience” and
“neurophilosophy” has become clearer, due primarily to more questions
now being pursued in both areas. Philosophy of neuroscience still
tends to pose traditional questions from philosophy of science
specifically about neuroscience. Such questions include: What is the
nature of neuroscientific explanation? And, what is the nature of
discovery in neuroscience? Answers to these questions can be pursued
either descriptively (how does neuroscience proceed?) or normatively
(how should neuroscience proceed)? Normative projects in philosophy of
neuroscience can be deconstructive, by criticizing claims made by
neuroscientists. For example, philosophers of neuroscience might
criticize the conception of personhood assumed by researchers in
cognitive neuroscience (cf. Roskies 2009). Normative projects can also
be constructive, by proposing theories of neuronal phenomena or
methods for interpreting neuroscientific data. These latter projects
are often integrated with theoretical neuroscience. For example, Chris
Eliasmith and Charles Anderson developed an approach to constructing
neurocomputational models in their book Neural Engineering
(2003). In separate publications, Eliasmith has argued that the
framework introduced in Neural Engineering provides both a
normative account of neural representation and a framework for
unifying explanation in neuroscience (cf. Eliasmith 2009; Eliasmith
2009).

Neurophilosophy still applies findings from the neurosciences to
traditional, mainstream philosophical questions. Examples now include:
What is an emotion? (Prinz 2004) What is the nature of desire?
(Schroeder 2004) How is social cognition made possible? (Goldman
2006)What is the neural basis of moral cognition? (Prinz 2007) What is
the neural basis of happiness? (Flanagan 2009) Neurophilosophical
answers to these questions are constrained by what neuroscience
reveals about nervous systems. For example, in his book Three
Faces of Desire, Timothy Schroeder argues that our commonsense
conception of desire attributes to it three capacities: (1) the capacity
to reinforce behavior when satisfied, (2) the capacity to motivate
behavior, and (3) the capacity to determine sources of pleasure. Based
on evidence from the literature on dopamine function and reinforcement
learning theory, Schroeder argues that reward processing is the basis
for all three capacities. Thus, reward is the essence of desire.

At present, there is a trend in neurophilosophy to look toward
neuroscience for guidance in moral philosophy. That should be evident
from the themes we've just mentioned. Simultaneously, there has arisen
interest in moralizing about neuroscience and neurological treatment
(see Levy 2007; Roskies 2009). The new field of neuroethics combines
both interest in the relevance of neuroscience data for understanding
moral cognition and the relevance of moral philosophy for regulating
the application of knowledge from neuroscience. The regulatory branch
of neuroethics focuses on the ethics of treatment for people who
suffer from neurological impairments, the ethics of attempts to
enhance human cognitive performance (Schneider 2009), the ethics of
applying “mind reading” technology to problems in forensic
science (Farah and Wolpe 2004), and the ethics of animal
experimentation in neuroscience (Farah 2008).

Other recent trends, now in philosophy of neuroscience, include
renewed interest in the nature of mechanistic explanations, given the
widespread use of the term among neuroscientists. In his book,
Explaining the Brain (2005), Carl Craver contends that
mechanistic explanations in neuroscience are causal and typically
multi-level. For example, the explanation of the neuronal action
potential involves the action potential itself, the cell in which it
occurs, electro-chemical gradients, and the proteins through which
ions flow. Here we have a composite entity (a cell) causally
interacting with neurotransmitters at its receptors. Parts of the cell
engage in various activities (the opening and closing of ligand-gated
and voltage-gated ion channels) to produce a pattern of change (the
depolarizing current constituting the action potential). The
mechanistic explanation of the action potential countenances entities
at the cellular, molecular, and atomic levels, each of which are
causally relevant to producing the action potential. This causal
relevance can be confirmed by altering any one of these variables
(e.g. the density of ion channels in the cell membrane) to generate
alterations in the action potential, and by verifying the consistency
of the purported invariance between the variables. (For challenges to
Craver's account of mechanistic explanation in neuroscience,
specifically concerning the action potential, see Weber 2008 and Bogen
2005.)

According to epistemic norms shared by neuroscientists, good
explanations in neuroscience are good mechanistic explanations, and
good mechanistic explanations are those that pick out invariant
relationships between mechanisms and the phenomena they control. (For
fuller treatment of invariance in causal explanations throughout
science, see James Woodward 2003.) Craver's account raises questions
about the place of reduction in neuroscience. John Bickle (2003)
suggests that the working concept of reduction in the neurosciences
consists of the discovery of systematic relationships between
interventions at lower levels of organization (as they are recognized
in cellular and molecular neuroscience) and higher level behavioral
effects (as they are described in psychology). Bickle calls this
perspective “reductionism-in-practice” to contrast it with
the concepts of intertheoretic or metaphysical reduction that have
been the focus of many debates in the philosophy of science and
philosophy of mind. Despite Bickle's reformulation of reduction,
mechanists generally resist the “reductionist” label. Is
mechanism a kind of reductionism-in-practice? Or does mechanism, as a
position on neuroscientific explanation, assume some type of autonomy
for psychology? If it does, reductionists can challenge mechanists on
this assumption. On the other hand, Bickle's reductionism-in-practice
clearly departs from inter-theoretic reduction, as the latter is
understood in philosophy of science. As Bickle himself acknowledges,
his latest reductionism was inspired heavily by mechanists' criticisms
of his earlier “new wave” account. Mechanists can
challenge Bickle that his departure from the traditional accounts has
also led to a departure from the interests that motivated those
accounts. (See Polger 2004 for a related challenge.)

The role of temporal representation in conscious experience and the
kinds of neural architectures sufficient to represent objects in time
has generated recent interest. In the tradition of Husserl's
phenomenology, Dan Lloyd (2002, 2003) and Rick Grush (2001, 2009) have
separately drawn attention to the tripartite temporal structure of
phenomenal consciousness as an explanandum for neuroscience. This
structure consists of a subjective present, an immediate past, and an
expectation of the immediate future. For example, one's conscious
awareness of a tune is not just of a time-slice of tune-impression,
but of a note that a moment ago was present, another that is now
present, and an expectation of subsequent notes in the immediate
future. As this experience continues, what was a moment ago temporally
immediate is now retained as a moment in the immediate past, what was
expected either occurred or didn't in what has now become the
experienced present, and a new expectation has formed of what will
come. One's experience is not static, even though the experience is of
a single object (the tune).

According to Lloyd, the tripartite structure of consciousness raises a
unique problem for analyzing fMRI data and designing experiments. The
problem stems from the tension between the sameness in the object of
experience (e.g. the same tune through its progression) and the
temporal fluidity of experience itself (e.g. the transitions between
heard notes). A standard means of analyzing fMRI data consists in
averaging several data sets and subtracting an estimate of baseline
activation from the composites (discussed in an earlier section of
this entry). This is done to filter noise from the task-related
hemodynamic response. But this practice ignores much of the data
necessary for studying the neural correlates of consciousness. It
produces static images that neglect the relationships between data
points in the time course. Lloyd instead applies a multivariate
approach to studying fMRI data, under the assumption that a recurrent
network architecture underlies the temporal processing that gives rise
to experienced time. A simple recurrent network has an input layer, an
output layer, a hidden layer, and an additional layer that copies the
prior activation state of either the hidden layer or the output
layer. Allowing the output layer to represent a predicted outcome, the
input layer can then represent a current state and the additional
layer a prior state. This assignment mimics the tripartite temporal
structure of experience in a network architecture. If the neuronal
mechanisms underlying conscious experience are approximated by
recurrent network architecture, one prediction is that current
neuronal states carry information about immediate future and prior
states. Applied to fMRI, the model predicts that time points in an
image series will carry information about prior and subsequent time
points. The results of Lloyd's (2002) analysis of 21 subjects' data
sets, sampled from the publicly accessible National fMRI Data Center,
support the prediction.

Grush's (2001, 2004) interest in temporal representation is part of
his broader systematic project addressing a semantic problem for
computational neuroscience, namely: how do we demarcate study of the
brain as an information processor from the study of any other complex
causal process? This question leads back into the familiar territory of
psychosemantics, but now the starting point is internal to the
practices of computational neuroscience. The semantic problem is
thereby rendered an issue in philosophy of neuroscience, insofar as it
asks: what does (or should) ‘computation’ mean in
computational neuroscience?

Grush's solution draws on concepts from modern control theory. In
addition to a controller, a sensor, and a goal state, certain kinds of
control systems employ a process model of the actual process
being controlled. A process model can facilitate a variety of
engineering functions, including overcoming delays in feedback and
filtering noise. The accuracy of a process model can be assessed
relative to its “plug-compatibility” with the actual
process. Plug-compatibility is a measure of the degree to which a
controller can causally couple to a process model to produce the same
results it would produce by coupling with the actual process. Note that
plug-compatibility is not an information relation.

To illustrate a potential neuroscientific implementation, Grush
considers a controller as some portion of the brain's motor systems
(e.g., premotor cortex). The sensors are the sense organs (e.g.,
stretch receptors on the muscles). A process model of the
musculoskeletal system might exist in the cerebellum (see Kawato 1999).
If the controller portion of the motor system sends spike trains to the
cerebellum in the same way that it sends spikes to the musculoskeletal
system, and if in return the cerebellum receives spike trains similar
to real peripheral feedback, then the cerebellum emulates the
musculoskeletal system (to the degree that the mock feedback resembles
real peripheral feedback). The proposed unit over which computational
operations range is the neuronal realization of a process model and its
components, or in Grush's terms an “emulator” and its
“articulants.”

The details of Grush's framework are too sophisticated to present in
short compass. (For example, he introduces a host of conceptual devices
to discuss the representation of external objects.) But in a nutshell,
he contends that understanding temporal representation begins with
understanding the emulation of the timing of sensorimotor
contingencies. Successful sequential behavior (e.g., spearing a fish)
depends not just on keeping track of where one is in space, but where
one is in a temporal order of movements and the temporal distance
between the current, prior, and subsequent movements. Executing a
subsequent movement can depend on keeping track of whether a prior
movement was successful and whether the current movement is matching
previous expectations. Grush posits emulators—process models in
the central nervous system—that anticipate, retain, and update
mock sensorimotor feedback by timing their output proportionally to
feedback from an actual process (see Grush forthcoming).

Lloyd's and Grush's approaches to studying temporal representation
are varied in their emphases. But they are unified in their implicit
commitment to localizing cognitive functions and decomposing them into
subfunctions using both top-down and bottom-up constraints. (See
Bechtel and Richardson 1993 for more details on this general
explanatory strategy.) Both develop mechanistic explanations that pay
little regard to disciplinary boundaries. One of the principal lessons
of Bickle's and Craver's work is that neuroscientific practice in
general is structured in this fashion. The ontological consequences of
adopting this approach are now being actively debated.

Given that philosophy of neuroscience, as other branches of philosophy
of science, has both descriptive and normative aims, it is critical to
develop methods for accurate estimation of current norms and practices
in neuroscience. Appeals to intuition will not suffice, nor will
single paradigm case studies do the job because those case studies may
fail to be representative. For example, an attempt to reconstruct the
conditions under which the mechanism of the action potential was
discovered may tell us little about the nature of discovery for other
neural mechanisms. But, large case samples may be difficult to log and
analyze. Furthermore, without protocols to guide such reconstructions,
the conclusions are susceptible to hidden biases, i.e. cherry picking
of data to support one's conclusions. Recent work by Alcino Silva,
Anthony Landreth, and John Bickle makes concrete proposals for
undertaking large-scale studies of the explanatory norms and the
growth of causal knowledge in neuroscience (see Silva, Landreth, and
Bickle forthcoming). They outline a framework for classifying,
documenting and analyzing experiments in neuroscience with practical
applications for planning relevant future experiments.

Bliss,T.V.P. and T. Lomo, 1973, “Long-Lasting Potentiation
of Synaptic Transmission in the Dentate Area of the Anaesthetized
Rabbit Following Stimulation of the Perforant Path,” Journal
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